Parental consent for newborn screening: a discrete choice experiment
Bibliographic record
Abstract
Background: Parental consent is very commonly assumed for newborn bloodspot screening (NBS) in most Canadian provincial screening programs. This falls short of usual norms, and evidence suggests that some parents would prefer an explicit process. This study was designed to inform improvements in NBS consent processes. Objectives: (1) To examine parents’ past experiences with, and attitudes towards, NBS consent processes in Canada. (2) To quantify parents’ preferences towards specific attributes of the NBS consent process, and identify characteristics of subgroups with different preference patterns. Method: A cross-sectional survey that included a discrete choice experiment (DCE) was conducted to capture information on participants’ past experiences with and preferences for NBS consent processes. DCE data were analyzed using conditional logit and latent class (LC) regression models. Results: The sample comprised 715 participants. As an overall group, respondents preferred to have NBS information provided late in pregnancy, for consent not to be assumed by providers, and for the consent decision to always be recorded. Three classes of participants with different underlying preference patterns were identified in the sample. Conclusion: If NBS programs wish to better meet parents’ preferenes, the results indicate specific aspects of the consent process that could be targeted for further examination..
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".